Data reconstruction methods may be used to reconstruct images from acquired data of an object. For example, in Computed Tomography (CT) imaging, projection rays are used as the acquired CT data to reconstruct images. Traditionally, images have been reconstructed from CT data using direct reconstruction algorithms such as filtered back projection (FBP) techniques. However, iterative reconstruction algorithms are also used in the reconstruction of images with application to CT images.
Iterative reconstruction methods are known to include statistical weighting for each projection ray in order to improve the signal-to-noise ratio or to provide other beneficial characteristics. For example, each ray may be weighted by an approximation to the inverse of the variance measurement of the ray. However, as the data is modified to correct for certain non-idealities (e.g., as the data is “prepped”), the variance of that data will be modified.
Thus, although statistical weighting can be used to increase the signal-to-noise ratio of a reconstruction, reasonable approximations of the variance measurements are needed in order to provide consistent results with varying tube voltage, patient size, gantry pre-filter (bowtie), etc. Known methods for determining approximations for the variances often do not result in completely accurate results, thus, reducing the robustness of the statistical iterative reconstruction.